3. Producción
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Item type:Publication, The Influence of Taxonomy and Environment on Leaf Trait Variation Along Tropical Abiotic Gradients(Frontiers Media S.A., 2020-03-03)Deconstructing functional trait variation and co-variation across a wide range of environmental conditions should increase the mechanistic understanding of community assembly processes and improve current parameterization of dynamic vegetation models. Here, we present a study that deconstructs leaf trait variation and co-variation to iithin-species, taxonomic-interspecific, and plot-environment components comparing three tropical environmental gradients in Peru, Brazil and Ghana. We measured photosynthetic, chemical and structural leaf traits using a standardized sampling protocol, totalling more than 1,000 individuals belonging to 367 species sampled. Variation associated with the whole interspecific taxonomic component (species+genus+family) for most traits was relatively consistent across environmental gradients, but intra-specificwithin-species variation and the plot-environment variation was strongly dependent on the environmental gradient. Trait-trait co-variation was also strongly linked to the environmental gradient where the traits were measured, although some traits had consistent co-variation components irrespective of environmental gradient. Our results demonstrate that filtering along gradients is mostly expressed through trait intra- and interspecifictaxonomic variation, but that trait co-variation is strongly dependent on the local environment, and thus global trait co-variation relationships might not always apply at smaller scales. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Functional susceptibility of tropical forests to climate change(Nature Research, 2022-07-01)Tropical forests are some of the most biodiverse ecosystems in the world, yet their functioning is threatened by anthropogenic disturbances and climate change. Global actions to conserve tropical forests could be enhanced by having local knowledge on the forestsʼ functional diversity and functional redundancy as proxies for their capacity to respond to global environmental change. Here we create estimates of plant functional diversity and redundancy across the tropics by combining a dataset of 16 morphological, chemical and photosynthetic plant traits sampled from 2,461 individual trees from 74 sites distributed across four continents together with local climate data for the past half century. Our findings suggest a strong link between climate and functional diversity and redundancy with the three trait groups responding similarly across the tropics and climate gradient. We show that drier tropical forests are overall less functionally diverse than wetter forests and that functional redundancy declines with increasing soil water and vapour pressure deficits. Areas with high functional diversity and high functional redundancy tend to better maintain ecosystem functioning, such as aboveground biomass, after extreme weather events. Our predictions suggest that the lower functional diversity and lower functional redundancy of drier tropical forests, in comparison with wetter forests, may leave them more at risk of shifting towards alternative states in face of further declines in water availability across tropical regions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data(John Wiley and Sons Inc, 2022-08-01)Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30040 hectares) along a 3322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient and cost-effective manner.